TY - JOUR
T1 - Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence
AU - Holzinger, Andreas
AU - Dehmer, Matthias
AU - Emmert-Streib, Frank
AU - Cucchiara, Rita
AU - Augenstein, Isabelle
AU - Ser, Javier Del
AU - Samek, Wojciech
AU - Jurisica, Igor
AU - Díaz-Rodríguez, Natalia
N1 - Funding Information:
Andreas Holzinger acknowledges funding support from the Austrian Science Fund (FWF) , Project: P-32554 explainable Artificial Intelligence and from the European Union’s Horizon 2020 research and innovation program under grant agreement 826078 (Feature Cloud). This publication reflects only the authors’ view and the European Commission is not responsible for any use that may be made of the information it contains; Natalia Díaz-Rodríguez is supported by the Spanish Government Juan de la Cierva Incorporación contract ( IJC2019-039152-I ); Isabelle Augenstein’s research is partially funded by a DFF Sapere Aude research leader grant; Javier Del Ser acknowledges funding support from the Basque Government through the ELKARTEK program (3KIA project, KK-2020/00049 ) and the consolidated research group MATHMODE (ref. T1294-19); Wojciech Samek acknowledges funding support from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 965221 (iToBoS), and the German Federal Ministry of Education and Research (ref. 01IS18025 A , ref. 01IS18037I and ref. 0310L0207C ); Igor Jurisica acknowledges funding support from Ontario Research Fund (RDI 34876 ), Natural Sciences Research Council (NSERC 203475 ), CIHR Research Grant ( 93579 ), Canada Foundation for Innovation (CFI 29272 , 225404 , 33536 ), IBM, Ian Lawson van Toch Fund, the Schroeder Arthritis Institute via the Toronto General and Western Hospital Foundation.
Funding Information:
We are very grateful for the valuable comments of the anonymous reviewers and the editor, which helped us a lot in revising this paper. Furthermore, we thank our colleagues from the international research community for their critical comments and suggestions for improvement. Andreas Holzinger acknowledges funding support from the Austrian Science Fund (FWF), Project: P-32554 explainable Artificial Intelligence and from the European Union's Horizon 2020 research and innovation program under grant agreement 826078 (Feature Cloud). This publication reflects only the authors? view and the European Commission is not responsible for any use that may be made of the information it contains; Natalia D?az-Rodr?guez is supported by the Spanish Government Juan de la Cierva Incorporaci?n contract (IJC2019-039152-I); Isabelle Augenstein's research is partially funded by a DFF Sapere Aude research leader grant; Javier Del Ser acknowledges funding support from the Basque Government through the ELKARTEK program (3KIA project, KK-2020/00049) and the consolidated research group MATHMODE (ref. T1294-19); Wojciech Samek acknowledges funding support from the European Union's Horizon 2020 research and innovation program under grant agreement No. 965221 (iToBoS), and the German Federal Ministry of Education and Research (ref. 01IS18025 A, ref. 01IS18037I and ref. 0310L0207C); Igor Jurisica acknowledges funding support from Ontario Research Fund (RDI 34876), Natural Sciences Research Council (NSERC 203475), CIHR Research Grant (93579), Canada Foundation for Innovation (CFI 29272, 225404, 33536), IBM, Ian Lawson van Toch Fund, the Schroeder Arthritis Institute via the Toronto General and Western Hospital Foundation.
Publisher Copyright:
© 2021 The Authors
PY - 2022/3
Y1 - 2022/3
N2 - Medical artificial intelligence (AI) systems have been remarkably successful, even outperforming human performance at certain tasks. There is no doubt that AI is important to improve human health in many ways and will disrupt various medical workflows in the future. Using AI to solve problems in medicine beyond the lab, in routine environments, we need to do more than to just improve the performance of existing AI methods. Robust AI solutions must be able to cope with imprecision, missing and incorrect information, and explain both the result and the process of how it was obtained to a medical expert. Using conceptual knowledge as a guiding model of reality can help to develop more robust, explainable, and less biased machine learning models that can ideally learn from less data. Achieving these goals will require an orchestrated effort that combines three complementary Frontier Research Areas: (1) Complex Networks and their Inference, (2) Graph causal models and counterfactuals, and (3) Verification and Explainability methods. The goal of this paper is to describe these three areas from a unified view and to motivate how information fusion in a comprehensive and integrative manner can not only help bring these three areas together, but also have a transformative role by bridging the gap between research and practical applications in the context of future trustworthy medical AI. This makes it imperative to include ethical and legal aspects as a cross-cutting discipline, because all future solutions must not only be ethically responsible, but also legally compliant.
AB - Medical artificial intelligence (AI) systems have been remarkably successful, even outperforming human performance at certain tasks. There is no doubt that AI is important to improve human health in many ways and will disrupt various medical workflows in the future. Using AI to solve problems in medicine beyond the lab, in routine environments, we need to do more than to just improve the performance of existing AI methods. Robust AI solutions must be able to cope with imprecision, missing and incorrect information, and explain both the result and the process of how it was obtained to a medical expert. Using conceptual knowledge as a guiding model of reality can help to develop more robust, explainable, and less biased machine learning models that can ideally learn from less data. Achieving these goals will require an orchestrated effort that combines three complementary Frontier Research Areas: (1) Complex Networks and their Inference, (2) Graph causal models and counterfactuals, and (3) Verification and Explainability methods. The goal of this paper is to describe these three areas from a unified view and to motivate how information fusion in a comprehensive and integrative manner can not only help bring these three areas together, but also have a transformative role by bridging the gap between research and practical applications in the context of future trustworthy medical AI. This makes it imperative to include ethical and legal aspects as a cross-cutting discipline, because all future solutions must not only be ethically responsible, but also legally compliant.
KW - Artificial intelligence
KW - Explainability
KW - Explainable AI
KW - Graph-based machine learning
KW - Information fusion
KW - Medical AI
KW - Neural-symbolic learning and reasoning
KW - Robustness
KW - Trust
U2 - 10.1016/j.inffus.2021.10.007
DO - 10.1016/j.inffus.2021.10.007
M3 - Article
AN - SCOPUS:85119285419
SN - 1566-2535
VL - 79
SP - 263
EP - 278
JO - INFORMATION FUSION
JF - INFORMATION FUSION
ER -